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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Wang, Pengyu Zhu, Hailong Zhang, Huayu Huang, Tao Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories NanJing China

ISBN: (纸本)9798350363760

Time-sensitive networking introduces deterministic latency and low jitter to Ethernet through traffic shaping mechanisms. The IEEE 802.1Qbv standard ensures deterministic transmission of critical flows via the time-aware shaper, where synthesizing the Gate Control List (GCL) schedule is a core challenge. Most SMT-based schedule synthesis algorithms consider only single-period scheduling under a unified timeline, leading to low schedulability. In this paper, we propose a cross-period scheduling algorithm that jointly schedules task and network, ensuring the correctness of cross-period scheduling constraints under a unified timeline through reasonable period mapping. In our defined task tree model, we introduce an optimization objective to minimize the end-to-end latency of task trees. Finally, we evaluate the performance of our algorithm. The results demonstrate that our algorithm significantly improves schedulability and achieves lower end-to-end latency compared to existing algorithms. Moreover, our algorithm can complete the computation within a reasonable time, even in large-scale networks. © 2024 IEEE.

关键词： scheduling algorithms

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Optimizing Round Robin scheduling with DBSCAN Clustering and Machine Learning

Optimizing Round Robin Scheduling with DBSCAN Clustering and...

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2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Hossain, Md Mehrab Biva, Nanziba Khan Nahid, Nafiz Rifat, Ariful Islam Islam, Ashraful Department of Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh Department of Computer Science and Engineering Faridpur Engineering College Faridpur Bangladesh Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Computer Science University of Regina Regina Canada

ISBN: (纸本)9798331510213

In the intricate realm of operating systems, scheduling algorithms play a pivotal role in resource allocation and process completion, directly impacting overall system performance. The quest for an efficient and optimized scheduling algorithm is perpetual in the pursuit of optimal operating system utilization. The Round Robin algorithm, known for its robust time-sharing methodology, emerges as a stalwart in the scheduling context. Its versatility spans various domains, making it a preferred choice in both general-purpose computing environments and real-time systems. This study aims to elevate the performance of the Round Robin algorithm in terms of average waiting time and number of context switches without altering its core algorithmic structure. A novel approach is proposed, utilizing DBSCAN to group related processes and determine a more accurate and efficient time quantum. Departing from the conventional practice of using a single time quantum for the entire schedule, we divide the schedule into groups. The optimal time quantum for each group is derived through machine learning and deep learning algorithms, leveraging rigorous features extracted from the schedule. Notably, the LSTM model emerges as the top performer, achieving an impressive 97% accuracy. The proposed modified version consistently outshines its traditional counterpart. In 92% of cases, the modified version demonstrates superior performance in average waiting time while achieving a 96.5% improvement in context switching. Considering both metrics, the modified version showcases a notable enhancement in 87.8% of cases. This holistic assessment unveils a 5% reduction in average waiting time and a substantial 15% decrease in context switching, signifying a meaningful advancement in overall system performance. © 2024 IEEE.

关键词： scheduling algorithms

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Research on Intelligent Optimal scheduling Algorithm for Vehicle Exhaust Emission in Railway Transportation System 6th

Research on Intelligent Optimal Scheduling Algorithm for Veh...

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6th International Conference on Cyber Security Intelligence and Analytics, CSIA 2024

作者： Xu, Zixiang Zhou, Xiaokai Wang, Yishan School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Development of Energy and Power Engineering Tsinghua University Beijing100084 China School of Mechanical and Vehicular Engineering Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9783031882869

Railway transportation system faces the problem of tail gas emission while meeting the transportation demand, which puts forward higher requirements for energy saving and emission reduction. In order to meet this challenge, this paper proposes an intelligent optimal scheduling method based on improved genetic algorithm, and constructs a multi-objective optimization model to balance the transportation efficiency and tail gas emission. The effectiveness of the algorithm in reducing tailpipe emissions, improving transportation efficiency and shortening calculation time is verified through simulation experiments of actual railroad transportation network. The results show that the method has good optimization performance and practical application value, providing technical support for the green development of railroad transportation system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： scheduling algorithms

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Hierarchical scheduling Mechanisms in Multi-Level Fog Computing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第5期15卷 2824-2837页

作者： Maciel Peixoto, Maycon Leone Genez, Thiago A. L. Bittencourt, Luiz F. Fed Univ Bahia UFBA BR-13083970 Sao Paulo Brazil Univ Estadual Campinas UNICAMP Inst Comp BR-13083970 Sao Paulo Brazil European Bioinformat Inst European Mol Biol Lab Wellcome Genome Campus Hinxton CB10 1SD Cambs England

Delivering cloud-like computing facilities at the network edge provides computing services with ultra-low-latency access, yielding highly responsive computing services to application requests. The concept of fog computing has emerged as a computing paradigm that adds layers of computing nodes between the edge and the cloud, also known as micro data centers, cloudlets, or fog nodes. Based on this premise, this article proposes a component-based service scheduler in a cloud-fog computing infrastructure comprising several layers of fog nodes between the edge and the cloud. The proposed scheduler aims to satisfy the application's latency requirements by deciding which services components should be moved upwards in the fog-cloud hierarchy to alleviate computing workloads at the network edge. One communication-aware policy is introduced for resource allocation to enforce resource access prioritization among applications. We evaluate the proposal using the well-known iFogSim simulator. Results suggest that the proposed component-based scheduling algorithm can reduce average delays for application services with stricter latency requirements while still reducing the total network usage when applications exchange data between the components. Results have shown that our policy was able to, on average, reduce the overload impact on the network usage by approximately 11 percent compared to the best allocation policy in the literature while maintaining acceptable delays for latency-sensitive applications.

关键词： Data centers scheduling algorithms Computer architecture Data transfer Dispatching Delays Resource management Fog computing cloud computing Internet of Things allocation

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Dependency-Aware Joint Task Offloading and Resource Allocation in Heterogeneous Mobile Edge Computing

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第12期23卷 19444-19458页

作者： Zhang, Guo Zhang, Baoxian Peng, Shuo Li, Cheng Univ Chinese Acad Sci Res Ctr Ubiquitous Sensor Networks Beijing 100049 Peoples R China Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada

Mobile edge computing (MEC) is a promising computing paradigm and can effectively reduce the energy consumption and computing costs at mobile devices by offloading computation-intensive and latency-sensitive applications/tasks to edge servers. However, how to achieve cost-effective dependent task offloading and resource allocation subject to application completion time constraint and service configuration constraint at edge side in heterogeneous MEC environments remains a challenge. To address this challenge, in this paper, we study the multi-application dependent task offloading and resource allocation problem in heterogeneous MEC environments for jointly minimizing the energy consumption and computing cost. We first formulate this problem as a mixed integer nonlinear programming (MINLP) problem. We propose a two-stage alternating optimization algorithm. In the first stage, a genetic-based algorithm is proposed to determine an optimized task offloading profile for given transmit power matrix, a look ahead based task scheduling algorithm is designed to obtain an optimized task schedule for the profile. In the second stage, the transmit power allocation problem for a given offloading profile is solved using convex optimization techniques. Extensive simulation results show that the proposed algorithm can effectively reduce the total cost of task executions as compared with baseline algorithms.

关键词： Servers Resource management Costs Energy consumption Optimization Mobile handsets Delays Computational modeling scheduling algorithms Wireless communication Mobile edge computing energy consumption computing cost dependent task offloading resource allocation

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Research on Ship Crew scheduling Algorithm Based on Improved Grey Wolf Optimization 4

Research on Ship Crew Scheduling Algorithm Based on Improved...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Tang, Lin Ying, Wenwei Huang, Meng Zou, Yu Unit 91977 of the Pla Beijing China

ISBN: (纸本)9798350363586

Crew scheduling in civilian ships is a combinatorial optimization problem with various constraints. Traditional methods struggle with large-scale scheduling, while classic algorithms often fail to balance performance and practicality. This paper presents an Improved Grey Wolf Optimization (IGWO) algorithm. We establish a mixed-integer programming model that considers hard and soft constraints to maximize crew-position matching. The IGWO algorithm enhances the standard Grey Wolf Optimization (GWO) framework by integrating strategies like heuristic initialization, adaptive encirclement search, elite leader selection, and constraint violation repair. Simulation on random and real-world datasets show that IGWO outperforms manual methods, rule-based heuristics, and the standard GWO algorithm in objective function value, constraint satisfaction, and employee satisfaction. This research advances heuristic optimization in combinatorial problems. © 2024 IEEE.

关键词： scheduling algorithms

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Design of Task Distribution and scheduling Strategies for Data Processingin the Internet of Things 16

Design of Task Distribution and Scheduling Strategies for Da...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Li, Qiang He, Keqiang Guo, Xing Deqing Alpha Innovation Research Institute University of Science and Technology of China Hefei China School of Computer Science and Technology Anhui University Hefei China Deqing Alpha Innovation Research Institute Anhui University Hefei China

ISBN: (纸本)9798350390643

To adapt to the characteristics of IoT tasks arriving online (i.e., the arrival pattern and time of tasks cannot be pre-dicted), delay sensitivity, and limited processing unit resources, to ensure the completion of tasks with low latency and to ensure the efficient and stable operation of the IoT platform, this paper studies the problem of online task distribution and scheduling in the unit manager of a data-driven Internet of Things (IoT) architecture. The problem is modeled and an efficient online task distribution and scheduling algorithm is proposed, named OnSche. We have validated the performance of the OnSche algorithm using real datasets and conducted a thorough verification. The experimental results show that under different parameter changes, OnSche is consistently superior to the baseline methods. © 2024 IEEE.

关键词： scheduling algorithms

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Hybrid Variable Neighborhood Search Algorithm for the Multi-objective Distributed Permutation Flowshop scheduling Problem with Sequence-Dependent Setup Times

Hybrid Variable Neighborhood Search Algorithm for the Multi-...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： She, Mingzhe Qiao, Fei Ma, Yumin Wang, Junkai Ai, Jiakang Liu, Juan Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China College of Electronics and Information Engineering Tongji University Shanghai201804 China

ISBN: (纸本)9781665410205

This paper addresses the multi-objective distributed permutation flowshop scheduling problem with sequence-dependent setup times (MODPFSP_SDST), whose optimization objectives are the makespan, the total energy consumption and the noise emission. An effective hybrid variable neighborhood search (HVNS) algorithm is proposed for solving this problem. HVNS utilizes a probabilistic matrix to store structural information of the promising solution, and then employs matrix-based search operators to execute variable neighborhood search. Additionally, the objective-oriented local intensification search is integrated to further enhance solution quality. Simulations and comparisons demonstrate the effectiveness of HVNS in solving the MODPFSP_SDST. © 2024 IEEE.

关键词： scheduling algorithms

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Multipath Bound for DAG Tasks

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第5期44卷 1676-1689页

作者： He, Qingqiang Guan, Nan Zhao, Shuai Lv, Mingsong Great Bay Univ Sch Comp & Informat Technol Dongguan 523000 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

This article studies the response time bound of a directed acyclic graph (DAG) task. Recently, the idea of using multiple paths to bound the response time of a DAG task, instead of using a single longest path in previous results, was proposed and led to the so-called multipath bound. Multipath bounds can greatly reduce the response time bound and significantly improve the schedulability of DAG tasks. This article derives a new multipath bound and proposes an optimal algorithm to compute this bound. We further present a systematic analysis on the dominance and the sustainability of three existing multipath bounds and the proposed multipath bound. Our bound theoretically dominates and empirically outperforms all existing multipath bounds. What is more, the proposed bound is the only multipath bound that is proved to be self-sustainable.

关键词： Time factors Computational modeling Real-time systems scheduling algorithms Parallel processing Integrated circuits Design automation Sustainable development Job shop scheduling Directed acyclic graph Directed acyclic graph (DAG) task multipath bound real-time scheduling response time bound

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Research on traffic scheduling algorithm and parameter optimization based on load balancing in time-sensitive network 4

Research on traffic scheduling algorithm and parameter optim...

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4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Chen, Zhao Beijing Institute of Mechanical Equipment Beijing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Time-sensitive network (TSN) guarantees deterministic delivery of traffic by prioritizing it differently. Commonly used traffic scheduling algorithm is time-aware shaper (TAS) combined with credit-based shaper (CBS). Aiming at the problem of excessive reserved bandwidth for stream reservation (SR) traffic during CBS scheduling, a traffic scheduling strategy based on load balancing was proposed. And the reserved bandwidth parameters of the SR traffic were optimized after completing the modeling of its delay problem. This strategy sliced and encapsulated the SR traffic packets into equal-length data frames at the end nodes and made them uniformly transmitted during the sending cycle. It reduced the end-to-end worst response time of the SR traffic due to packet congestion. After that the delay model was solved using simulated annealing algorithm to get the optimal reserved bandwidth. Simulation results showed that the proposed SR traffic scheduling strategy effectively reduced the reserved bandwidth and the delay of traffic transmission inside the switch on the basis of satisfying the traffic schedulability. © 2024 SPIE.

关键词： scheduling algorithms

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